The performance of database systems, particularly enterprise database systems, depends on an effective configuration of physical design structures, such as indexes, in the databases that compose those systems. Automatically configuring such physical design structures to increase the performance of the underlying database system, generally referred to as an automated physical design problem, has been recently researched.
The physical design problem statement is traditionally stated as:                Given a workload W and a storage budget B, find the set of physical structures (that is, the configuration) that fits within B and results in the lowest execution cost for W.        
Thus, existing solutions to the automated physical design problem generally attempt to minimize execution costs of input workloads for a given a storage constraint. However, this model is not flexible enough to address several real-world situations. More particularly, a single storage constraint does not model many important situations in current database management system installations. What is needed is a generalized version of the physical design problem statement that accepts one or more complex constraints.